| var.test {stats} | R Documentation | 
F Test to Compare Two Variances
Description
Performs an F test to compare the variances of two samples from normal populations.
Usage
var.test(x, ...)
## Default S3 method:
var.test(x, y, ratio = 1,
         alternative = c("two.sided", "less", "greater"),
         conf.level = 0.95, ...)
## S3 method for class 'formula'
var.test(formula, data, subset, na.action, ...)
Arguments
| x,y | numeric vectors of data values, or fitted linear model
objects (inheriting from class  | 
| ratio | the hypothesized ratio of the population variances of
 | 
| alternative | a character string specifying the alternative
hypothesis, must be one of  | 
| conf.level | confidence level for the returned confidence interval. | 
| formula | a formula of the form  | 
| data | an optional matrix or data frame (or similar: see
 | 
| subset | an optional vector specifying a subset of observations to be used. | 
| na.action | a function which indicates what should happen when
the data contain  | 
| ... | further arguments to be passed to or from methods. | 
Details
The null hypothesis is that the ratio of the variances of the
populations from which x and y were drawn, or in the
data to which the linear models x and y were fitted, is
equal to ratio.
Value
A list with class "htest" containing the following components:
| statistic | the value of the F test statistic. | 
| parameter | the degrees of the freedom of the F distribution of the test statistic. | 
| p.value | the p-value of the test. | 
| conf.int | a confidence interval for the ratio of the population variances. | 
| estimate | the ratio of the sample variances of  | 
| null.value | the ratio of population variances under the null. | 
| alternative | a character string describing the alternative hypothesis. | 
| method | the character string
 | 
| data.name | a character string giving the names of the data. | 
See Also
bartlett.test for testing homogeneity of variances in
more than two samples from normal distributions;
ansari.test and mood.test for two rank
based (nonparametric) two-sample tests for difference in scale.
Examples
x <- rnorm(50, mean = 0, sd = 2)
y <- rnorm(30, mean = 1, sd = 1)
var.test(x, y)                  # Do x and y have the same variance?
var.test(lm(x ~ 1), lm(y ~ 1))  # The same.